The University of Southampton
University of Southampton Institutional Repository

Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning

Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning
Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning
In this paper, we demonstrated the use of the k-Nearest Neighbor, a machine learning algorithm, to identify mechanical vibration modes of a cantilever beam in a frequency range between 40-300 Hz at an accelerations of 1.1±0.1 g. We attached fiber Bragg gratings to the cantilever structure and analyzed the spectral response during vibration. We observe small increases in spectral bandwidth of three Bragg gratings to perform a 3-dimensional classification environment and evaluated the accuracy of the algorithm with independent testing data.
OSA
Jantzen, Senta Lisa
e532e171-8ea3-4576-8843-17d96a3995d4
Yu, Jiarui
024ed044-5693-452b-81e8-277837f371bf
Smith, Peter G.R.
8979668a-8b7a-4838-9a74-1a7cfc6665f6
Holmes, Christopher
16306bb8-8a46-4fd7-bb19-a146758e5263
Jantzen, Senta Lisa
e532e171-8ea3-4576-8843-17d96a3995d4
Yu, Jiarui
024ed044-5693-452b-81e8-277837f371bf
Smith, Peter G.R.
8979668a-8b7a-4838-9a74-1a7cfc6665f6
Holmes, Christopher
16306bb8-8a46-4fd7-bb19-a146758e5263

Jantzen, Senta Lisa, Yu, Jiarui, Smith, Peter G.R. and Holmes, Christopher (2020) Identifying mechanical vibration modes of a cantilever using spectrally multiplexed Bragg gratings and machine learning. In CLEO Pacific Rim. OSA. 2 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we demonstrated the use of the k-Nearest Neighbor, a machine learning algorithm, to identify mechanical vibration modes of a cantilever beam in a frequency range between 40-300 Hz at an accelerations of 1.1±0.1 g. We attached fiber Bragg gratings to the cantilever structure and analyzed the spectral response during vibration. We observe small increases in spectral bandwidth of three Bragg gratings to perform a 3-dimensional classification environment and evaluated the accuracy of the algorithm with independent testing data.

Text
Jantzen_CLEO_PR_kNN
Download (2MB)

More information

Published date: 14 July 2020
Venue - Dates: 14th Pacific Rim Conference on Lasers and Electro-Optics (CLEO PR 2020): (Virtual Conference), Australia, 2020-08-03 - 2020-08-05

Identifiers

Local EPrints ID: 442834
URI: http://eprints.soton.ac.uk/id/eprint/442834
PURE UUID: 23edcfb5-d1ab-4ccc-bf6b-8d775387a3b0
ORCID for Senta Lisa Jantzen: ORCID iD orcid.org/0000-0003-2646-7293
ORCID for Peter G.R. Smith: ORCID iD orcid.org/0000-0003-0319-718X
ORCID for Christopher Holmes: ORCID iD orcid.org/0000-0001-9021-3760

Catalogue record

Date deposited: 28 Jul 2020 16:32
Last modified: 03 Aug 2020 04:01

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×